The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature.

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

Combining Allograft Adipose and Fascia Matrix as an Off-the-Shelf Scaffold for Adipose Tissue Engineering Stimulates Angiogenic Responses and Activates a Proregenerative Macrophage Profile in a Rodent Model.

OBJECTIVE: Bioscaffolds for treating soft tissue defects have limitations. As a bioscaffold, allograft adipose matrix (AAM) is a promising approach to treat soft tissue defects. Previously, we revealed that combining superficial adipose fascia matrix with AAM, components of the hypodermis layer of adipose tissue, improved volume retention, adipogenesis, and angiogenesis in rats 8 weeks after it was implanted compared with AAM alone. Here, we modified the fascia matrix and AAM preparation, examined the tissue over 18 weeks, and conducted a deeper molecular investigation. We hypothesized that the combined matrices created a better scaffold by triggering angiogenesis and proregenerative signals. METHODS: Human AAM and fascia matrix were implanted (4 [1 mL] implants/animal) into the dorsum of male Fischer rats (6-8 weeks old; ~140 g) randomly as follows: AAM, fascia, 75/25 (AAM/fascia), 50/50, and 50/50 + hyaluronic acid (HA; to improve extrudability) (n = 4/group/time point). After 72 hours, as well as 1, 3, 6, 9, 12, and 18 weeks, graft retention was assessed by a gas pycnometer. Adipogenesis (HE), angiogenesis (CD31), and macrophage infiltration (CD80 and CD163) were evaluated histologically at all time points. The adipose area and M1/M2 macrophage ratio were determined using ImageJ. RNA sequencing (RNA-seq) and bioinformatics were conducted to evaluate pathway enrichments. RESULTS: By 18 weeks, the adipose area was 2365% greater for 50/50 HA (281.6 ± 21.6) than AAM (11.4 ± 0.9) (P < 0.001). The M1/M2 macrophage ratio was significantly lower for 50/50 HA (0.8 ± 0.1) than AAM (0.9 ± 0.1) at 6 weeks (16%; P < 0.05). This inversely correlated with adipose area (r = -0.6; P > 0.05). The RNA-seq data revealed that upregulated adipogenesis, angiogenesis, and macrophage-induced tissue regeneration genes were temporally different between the groups. CONCLUSIONS: Combining the fascia matrix with AAM creates a bioscaffold with an improved retention volume that supports M2 macrophage-mediated angiogenesis and adipogenesis. This bioscaffold is worthy of further investigation.

Gaussian Surface Curvature Mapping Indicating High Risk Type B Thoracic Aortic Dissections.

BACKGROUND: Identifying fragile aortas that are more likely to lead to adverse clinical outcomes would provide surgeons with a better sense of how to balance the risks of surgical versus medical management in patients with type B dissections. We examine the progression of a type B dissection into a type A dissection in a patient and analyze changes in the Gaussian surface curvature distribution, as well as the response of the stress distribution at the lesser curve in response to pressurization. We hypothesize that examining the Gaussian curvature will provide us with a link between aortic surface geometry and the stress distribution, which is crucial to understanding the process driving aortic dissection. METHODS: Computed tomography scans of a patient before and after the type A dissection are obtained. These are segmented in Simpleware ScanIP. Centerline curvatures are calculated on segmented models in ScanIP. Models are then pressurized in the finite element analysis software Abaqus. The Gaussian curvature is calculated by exporting segmentations into the computational program Matlab and applying a modified previously published algorithm. RESULTS: The centerlines generated in ScanIP fail to capture the change in the acuity of the lesser curve before and after the type A dissection. Instead, Gaussian curvature analysis shows that the curvature distribution before the type A dissection is much wider compared with the distribution after the type A dissection. In addition, analyzing the stress distribution in response to pressurization reveals that before the type A dissection there is a large divergence in the principal stress vectors at the lesser curve but this transitions to a more uniform hoop stress after the type A dissection. CONCLUSIONS: Our analysis demonstrates that Gaussian surface curvature analysis captures changes in aortic geometry that are otherwise silent in centerline curvature analysis. Here, we show that as the aorta develops a type A dissection it is able to more smoothly handle the hoop stress at the lesser curve compared with the stress focusing seen in the before type A geometry. We propose that the geometric focusing before type A creates a higher energy stress state, which is relaxed on retrograde dissection. Thus, Gaussian curvature analysis may provide a window to capture underlying geometric instability in type B dissections.

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution.

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area (δ𝒮 vs. ) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in δ𝒮 closely parallels the true geometric progression of aortic disease patients.

Computational analysis of endovascular aortic repair proximal seal zone preservation with endoanchors: A case study in cylindrical neck anatomy.

BACKGROUND: Endovascular aortic repair is the common approach for abdominal aortic aneurysms, but endoleaks remain a significant problem with long-term success. Endoanchors have been found to reduce the incidence of type 1A endoleaks and can treat intraoperative type 1a endoleaks. However, little is known about the optimal number and position of endoanchors to achieve the best outcome. METHODS: Using image segmentation and a computational model derived from a reconstructed native patient abdominal aortic aneurysm geometry, the stability of the proximal seal zone was examined through finite element analysis in Abaqus (Dassault Systèmes, Providence, RI). The biomechanical parameter of contact area was compared for varying numbers (0, 2, 4, 8) and positions (proximal, medial, distal) of endoanchors under different adhesion strengths and physiologic pressure conditions. RESULTS: In every simulation, an increase in adhesion strength is associated with maintenance of proximal seal. For biologically plausible adhesion strengths, under conditions of normal blood pressure (120 mm Hg), the addition of any number of endoanchors increases the stability of the endograft-wall interface at the proximal seal zone by approximately 10% compared with no endoanchors. At hypertensive pressures (200 mm Hg), endoanchors increase the stability of the interface by 20% to 60% compared with no endoanchors. The positioning of endoanchors within the proximal seal zone has a greater effect at hypertensive pressures, with proximal positioning increasing stability by 15% compared with medial and distal positioning and 30% compared with no endoanchors. CONCLUSIONS: Endoanchors improve fixation within the proximal seal zone particularly under conditions of high peak systolic pressure. Seal zone stabilization provides a mechanism through which endoanchor addition may translate into lower rates of type 1a endoleaks for patients.